[Interview] The Current Status Of AI In Drug Discovery And Looking Forward Into 2018

Without a doubt, the area of artificial intelligence (AI) has been a sensation lately -- judging by the amount of hype around this topic. But the hype is not a guarantee of a real breakthrough, which is defined by facts and measurable achievements, not just loud statements.

If it is not enough, here is a list of pretty much everything AI can do already today, speaking in practical terms. With this, it is becoming obvious that the progress in the AI space is quite real and the practical benefits are quite tangible, albeit there are a lot of technological and organizational challenges to overcome yet.

What is important to realize, though, is that the AI technologies hold a substantial disruptive potential, which can (possibly) transform the whole industries and redefine status quo. It is something to keep in mind if we talk about maintaining a long-term innovative competitiveness.

At BioPharmaTrend we are striving to find out what the AI-driven innovation really means for the biopharmaceutical industry, and drug discovery research in particular.

I decided to find out answers to some of the intriguing questions about AI in drug discovery/biopharma from the first hands.

Dr. Alex Zhavoronkov, co-founder, and CEO of Insilico Medicine -- a company leading the way in the applications of AI to drug discovery and age-related research, agreed to comment on what has been going on in this area lately, and what to expect in 2018.

Alex, what do you think was one biggest milestone in 2017 for the artificial intelligence advancement in drug discovery/development space?

I am very biased here since we pioneered this particular field of AI in drug discovery but I think that the application of the Generative Adversarial Networks (GANs) and combinations of GANs with Reinforcement Learning (RL) to the generation of novel molecular structures with the desired properties was the real breakthrough. When we managed to generate novel molecular entities with a desired set of pharmacological properties for a specific target, synthesize the molecule and then see this molecule perform as expected in the activity and phenotypic assays, we knew that now we can demonstrate the “Sputnik moment” to the pharmaceutical industry. And while most of the hype around Insilico is related to the other projects in AI, GAN- and GAN/RL- generation of molecules was our main breakthrough.

What are the main still unsolved challenges for the AI application in the drug discovery/development space? Will we see substantial progress towards overcoming them in 2018?

There are more challenges than I can list in an hour. Some of these challenges are related to validation, some are related to the general industry culture and some are technical.

The main challenge we are facing today is the gap between the cutting edge AI researchers, investors and the computational biologists or chemists and their managers within the pharma industry. Any company with a few senior and reputable people at the helm can call themselves AI for healthcare nowadays and investors will pour millions in the company even when their core technology is based on linear regression for a recommendation engine. Some of these are in the “digital medicine snake oil” trying to cure all diseases with the behavioral modifications. And within the pharma industry managers talk more about the limitations of AI and comparing deep learning with the more traditional ML algorithms even when we are not talking about the classification problems. Every pharma is trying to build up the internal expertise and is hiring data scientists. But the rate of progress is much slower than in the academia or in the startup world. The rate of progress we have at Insilico is so fast that when we publish a paper in a peer-reviewed journal, we already consider the project to be pretty much obsolete. And when the pharma reproduces the proof of concept from a year ago and starts talking about the limitations or variations on the theme, we are already at a different level.

Another problem is validation. Startups like ours are trying to validate our molecules or DL methods experimentally and often we do succeed. But while these validation experiments tell us that we are moving into the right direction and help improve the AI with every iteration, most often it is not enough to convince pharma to put their faith into a molecule. And when they do, the validation time required is very long.

When applying the deep learning techniques to images and videos validation is almost instantaneous and often you can see what to fix. This rapid pace of validation allowed for the driverless Tesla and for the deep-learned google translate. But when dealing with molecules or biomarkers, it takes a very long time.

The traditional pharma model is that for a specific disease you need one great target and for this target, you need one great molecule. So if we generate the perfect and safe molecule, but the target is wrong, the clinical trial will fail in phase II or phase III. And it is likely that the traditional pharma model is wrong, there is usually more than one target. And that is why another half of our company is developing AI to identify the best targets and generate molecules for sets of targets. But validation can only be performed in sequence: molecule-target, molecule-target-disease, a set of molecules-set of targets-disease.

It will take several years to validate the entire end-to-end AI-driven drug discovery and development process. And we are likely to get stuck with the same problem. To do end-to-end validation we will need pharma help and be prepared to fail quickly at the pre-clinical level and learn from the failures. My team operates on a premise that some of the problems may be more difficult than the others and some need more data and time to solve, but nothing is impossible. Pharma and VCs do not have the same attitude, so we have to try every possible way to validate and collaborate with as many groups worldwide as possible.

I do think that in 2018 we will see more progress in pharma AI. In 2015 everyone was skeptical, in 2016 we started doing a lot of pilots, in 2017 some of the pharma companies started making exploratory deals and in 2018 we are likely to see some results. I also hope that the companies like Facebook and Amazon realize their strengths and the real transformative changes they can induce in healthcare in both diagnostics and personalized medicine.

Then, do you think the year 2018 will be a “breakthrough year” for the AI in drug discovery/development? If so, what kind of goals can potentially be achieved in 2018?

Without a doubt. I think that 2017 was the breakthrough year. My only fear is that the global economy is going to go into a recession or even worse. Considering how long and fast it has been growing and the many new advances in fintech AI, there is a chance of a collapse much worse than in 2008. So we are kind of racing against the time and we are doing everything we can to develop AI that will take drug discovery to the next level.

In your opinion, is the hype around the AI in drug discovery harmful for the advancement of the industry in some ways?

My only concern is the definition of AI. There is definitely not enough hype in deep learning and reinforcement learning what we call next-gen AI. Some companies not deeply focused on next-gen AI may be overvalued and we may see some Theranos-like stories. And considering how few companies are in this field, it may drag everybody down. But on the grand scale of things, I think that the combined valuation of all the companies in AI for drug discovery is less than $3 billion dollars and I know of only one over a billion. The pharmaceutical companies make larger deals on the monthly basis, so the hype in AI for drug discovery is insignificant compared to the hype in AI in other areas. The real problem is that there are very few real AI companies on the market. Nvidia shortlisted us as the top 5 AI companies for social impact. Most of the other companies were in healthcare, but only ours in drug discovery. CB Insights picked us as the global top 100 AI companies, only 8 companies were in healthcare and only 2 in drug discovery. We need more hype and more competition in this area. And we need more sizable pharma deals to attract more people into the field. It is a much more altruistic endeavor than animating emoji on the mobile devices.

Finally, what is a unique offer your company develops? What are your major goals for 2018?

We position Insilico as the “Bell Labs” for pharma AI. Our core strength is agility and we predate on the advances in AI all over the world and apply these to drug discovery and biomarker development while developing our own approaches. We see what works, fail quickly and learn from the failures. Some of the ideas that work are commercialized and either licensed out or spun off. So there is a constant supply of unique offers. For example, we did not invent GANs, but we were the first to apply them to drug discovery and after a year of trials and errors we perfected the technique, developed our own system of molecular graph representations and started synthesizing and validating the molecules. Then we added reinforcement learning to generate the molecules to a specific objective. Now it is a very large and promising part of our business with several deals in the pipeline.

We also pioneered the application of deep neural networks for multi-modal biomarker development. We started with developing accurate predictors of age within every possible data type and then integrated these into a multi-modal predictor. Now we can work with the many age-related diseases and identify the most relevant features using the deep feature selection and permutation feature importance techniques and group these features into pathways. This project lead to a few business deals and we launched a range of nutraceutical products in partnership with a nutraceutical vendor that are already on the market. We also launched fun consumer projects like Young.AI, which predicts the customer's age using multiple data types and allows to track life data over time.

Our work in multi-modal biomarker development leads to a peculiar partnership with BitFury, #1 in blockchain outside China with a unique development team in 16 countries with over 160 top-notch blockchain coders in Ukraine. We realized that it is possible to do quality control, verification, dataset appraisal and many other tasks using our AI and built a blockchain- and AI- driven marketplace for human life data, which is currently being tested. We spun off a new company in Hong Kong called Longenesis, Inc (www.longenesis.com) and the marketplace is expected to launch in March 2018. The first joint paper with BitFury describing the concept was published in November.

One of the main goals for 2018 is to further improve the GAN/RL generators of new molecules and get more and larger licensing deals.

Another major goal is to launch the www.Longenesis.com life data marketplace. It will set a precedent for a secure and decentralized data sharing using blockchain technology and our AI algorithms.